Placement of wind turbines using genetic algorithms

A genetic algorithm approach is employed to obtain optimal placement of wind turbines for maximum production capacity while limiting the number of turbines installed and the acreage of land occupied by each wind farm. Specifically, three cases are considered—(a) unidirectional uniform wind, (b) uniform wind with variable direction, and (c) non-uniform wind with variable direction. In Case (a), 600 individuals are initially distributed over 20 subpopulations and evolve over 3000 generations. Case (b) has 600 individuals spread over 20 subpopulations initially and evolves for 3000 generations. Case (c) starts with 600 individuals spread over 20 subpopulations and evolves for 2500 generations. In addition to optimal configurations, results include fitness, total power output, efficiency of power output and number of turbines for each configuration. Disagreement with the results of an earlier study is observed and a possible explanation is provided.     

Optimization of Type 4 composite pressure vessels using genetic algorithms and simulated annealing

We propose and demonstrate two methodologies for weight minimization of Type 4 (plastic lined, fiber wrapped) compressed hydrogen pressure vessels: genetic algorithms and simulated annealing. We consider 70 MPa vessels with a safety factor of 2.25, and analyze the vessels with classical laminate theory. We propose an objective function based on Tsai-Wu criterion, composite thickness, a safety factor, and a penalization factor. The optimum results are analyzed and compared by a high resolution finite element model. Computer simulations show that the proposed methodology produces more efficient designs by reducing the weight by up to 9.8% and 11.2% when compared to previously published vessel optimization research.     

Parameter extraction of fuel cells using hybrid interior search algorithm

Precise modelling of fuel cells is very important for understanding their functioning. In this work, an application of hybrid interior search algorithm (HISA) is proposed to extract the parameters of fuel cells for their electromechanical equations based on nonlinear current‐voltage characteristics. Proposed hybridised algorithm has been developed using evolutionary mutation and crossover operators so as to enhance the modelling capability of interior search algorithm (ISA). To assess the modelling performance of HISA, parameter extraction of two types of fuel cell models, namely, proton exchange membrane fuel cell (PEMFC) and solid oxide fuel cell (SOFC) have been considered. Modelling performance of HISA, assessed using mean squared error between computed and experimental data, is found to be superior to ISA and several other recently reported prominent optimisation methods. Based on the presented intensive simulation investigations, it is concluded that HISA improves the performance of the basic ISA in terms of fitter solutions, robustness, and convergence rate and therefore offers a promising optimisation technique for parameter extraction of fuel cells.     

Optimization of polymer electrolyte membrane fuel cell flow channels using a genetic algorithm

The design of the flow channels in PEM fuel cells directly impacts the transport of reactant gases to the electrodes and affects cell performance. This paper presents results from a study to optimize the geometry of the flow channels in a PEM fuel cell. The optimization process implements a genetic algorithm to rapidly converge on the channel geometry that provides the highest net power output from the cell. In addition, this work implements a method for the automatic generation of parameterized channel domains that are evaluated for performance using a commercial computational fluid dynamics package from ANSYS. The software package includes GAMBIT as the solid modeling and meshing software, the solver FLUENT, and a PEMFC Add-on Module capable of modeling the relevant physical and electrochemical mechanisms that describe PEM fuel cell operation. The result of the optimization process is a set of optimal channel geometry values for the single-serpentine channel configuration. The performance of the optimal geometry is contrasted with a sub-optimal one by comparing contour plots of current density, oxygen and hydrogen concentration. In addition, the role of convective bypass in bringing fresh reactant to the catalyst layer is examined in detail. The convergence to the optimal geometry is confirmed by a bracketing study which compares the performance of the best individual to those of its neighbors with adjacent parameter values.     

Design and control strategies of PV-Diesel systems using genetic algorithms

Hybrid photovoltaic systems (PV-hybrid) use photovoltaic energy combined with other sources of energy, like wind or Diesel. If these hybrid systems are optimally designed, they can be more cost effective and reliable than PV-only systems. However, the design of hybrid systems is complex because of the uncertain renewable energy supplies, load demands and the non-linear characteristics of some components, so the design problem cannot be solved easily by classical optimisation methods. When these methods are not capable of solving the problem satisfactorily, the use of heuristic techniques, such as the Genetic Algorithms, can give better results.The authors have developed the HOGA program (Hybrid Optimisation by Genetic Algorithms), a program that uses a Genetic Algorithm (GA) to design a PV-Diesel system (sizing and operation control of a PV-Diesel system). The program has been developed in C++.In this paper a PV-Diesel system optimised by HOGA is compared with a stand-alone PV-only system that has been dimensioned using a classical design method based on the available energy under worst-case conditions. In both cases the demand and the solar irradiation are the same. The computational results show the economical advantages of the PV-hybrid system. HOGA is also compared with a commercial program for optimisation of hybrid systems.Furthermore, we show a number of results and conclusions about hybrid systems optimised by HOGA.     

Optimization design of slotted fin by numerical simulation coupled with genetic algorithm

Using a novel method that couples genetic algorithm (GA) with numerical simulation, the geometric configuration for a two-dimensional slotted fin has been optimized in this paper. The objective of optimization is to maximize the heat transfer capacity of slotted fin, and minimize the pressure drop penalty of fluid flow through the fin. The key of this method is the fitness function of GA, which were (j/j₀)/(f/f₀) and j/j₀. In this complex multiparameter problem, the numerical simulation is a crucial step to calculate the Colburn factor j and friction factor f. The results showed that for two-dimensional slotted fin considered, the j factor is increased by 229.22%, the f factor is increased by 196.30%, and the j/f ratio was increased by 11.11% at Re = 500 based on optimal integrated performance (j/j₀)/(f/f₀); the j factor is increased by 479.08% at Re = 500 based on optimal heat exchange capacity j/j₀. The feasibility of optimal designs was verified by the field synergy principle.     

System network planning expansion using mathematical programming, genetic algorithms and tabu search

In this paper, system network planning expansion is formulated for mixed integer programming, a genetic algorithm (GA) and tabu search (TS). Compared with other optimization methods, GAs are suitable for traversing large search spaces, since they can do this relatively rapidly and because the use of mutation diverts the method away from local minima, which will tend to become more common as the search space increases in size. GA’s give an excellent trade off between solution quality and computing time and flexibility for taking into account specific constraints in real situations. TS has emerged as a new, highly efficient, search paradigm for finding quality solutions to combinatorial problems. It is characterized by gathering knowledge during the search and subsequently profiting from this knowledge. The attractiveness of the technique comes from its ability to escape local optimality. The cost function of this problem consists of the capital investment cost in discrete form, the cost of transmission losses and the power generation costs. The DC load flow equations for the network are embedded in the constraints of the mathematical model to avoid sub-optimal solutions that can arise if the enforcement of such constraints is done in an indirect way. The solution of the model gives the best line additions and also provides information regarding the optimal generation at each generation point. This method of solution is demonstrated on the expansion of a 10 bus bar system to 18 bus bars. Finally, a steady-state genetic algorithm is employed rather than generational replacement, also uniform crossover is used.     

Design optimization of insulation usage and space conditioning load using energy simulation and genetic algorithm

Architectural design is a process to find the best solution to satisfy various design criteria. To achieve sustainable and green design, performance simulations are often used to verify these criteria and modify the design. The conventional approach of manual trial-and-error is too time-consuming to be practical. Introducing optimization technique can greatly improve the design efficiency and help designers find the optimal design. In this paper, modeFRONTIER was used as the design optimization environment to find the best insulation strategy to minimize the space conditioning load of an office building located in Nanjing, China while keeping the insulation usage at minimum. EnergyPlus was integrated into the optimization tool by writing a DOS batch file to automate the work flow. The search engine was the genetic algorithm and it proved to be able to generate a well-defined Pareto frontier in a reasonable number of runs. Based on the Pareto frontier, the designer can specify his preferences and select the final design. The case study shows that an energy simulation program can be effectively integrated into a design optimization environment to find the optimal design. The technique presented has a broad application in architectural design, especially when the design considerations are multi-objective.     

Model parameters identification of the PEMFCs using an improved design of Crow Search Algorithm

There is an increasing trend for fuel cell systems applications in electricity generation systems instead of traditional power generation systems because of their advantages such as high efficiency and almost no environmental pollution, desirable dynamic response, and reliability. Due to this reason, herein, a new method has been presented for optimum identification of the model of the proton exchange membrane fuel cell (PEMFC) model. The major concept is to lessen the sum of squared error (SSE) amount of the observed output voltage and the output voltage of the PEMFC stack by an improved version of Crow Search optimizer (ICSO). To validate the suggested technique, it is applied to two studied cases and the achievements are put in comparison with several newest optimizers, which are Genetic algorithm (GA), Grasshopper Optimizer (GHO), and Salp Swarm Optimizer (SSO). The achievements show that the suggested ICSO gives a better superiority to the other comparative algorithms for optimum estimation of the PEMFC model.     

An adaptive RNA genetic algorithm for modeling of proton exchange membrane fuel cells

The accurate mathematical model is the key issue to simulation and design of the fuel cell power systems. Aiming at estimating the proton exchange membrane fuel cell (PEMFC) model parameters, an adaptive RNA genetic algorithm (ARNA-GA) which is inspired by the mechanism of biological RNA is proposed. The ARNA-GA uses the RNA strands to represent the potential solutions and new genetic operators are designed for improving the global searching ability. In order to maintain the population diversity and avoid premature convergence, on the basis of the dissimilarity coefficient, the adaptive genetic strategy that allows the algorithm dynamically select crossover operation or mutation operation to execute is proposed. Numerical experiments have been conducted on some benchmark functions with high dimensions. The results indicate that ARNA-GA has better search capability and a higher quality of solutions. Finally, the proposed approach has been applied for the parameter estimation of PEMFC model and the satisfactory results are reached.     

基于遗传算法的电力系统综合负荷模型的建模与仿真

遗传算法是一种具有全局寻优能力的随机搜索算法,但其本身存在收敛速度慢和易早熟的缺陷。为此,引入一种改进的遗传算法用于电力负荷综合建模。该算法具有克服早熟、避免近亲繁殖和自适应的优良特性。应用建模实例表明,遗传算法辨识所得负荷模型的描述精度很高,其模型参数呈现很好的稳健性,从而有效地克服了传统优化方法的模型参数分散性。     

Simulation of biological hydrogen production in a UASB reactor using neural network and genetic algorithm

In this study the performance of a granule-based H₂-producing upflow anaerobic sludge blanket (UASB) reactor was simulated using neural network and genetic algorithm. A model was designed, trained and validated to predict the steady-state performance of the reactor. Organic loading rate, hydraulic retention time (HRT), and influent bicarbonate alkalinity were the inputs of the model, whereas the output variables were one of the following: H₂ concentration, H₂ production rate, H₂ yield, effluent total organic carbon, and effluent aqueous products including acetate, propionate, butyrate, valerate, and caporate. Training of the model was achieved using a large amount of experimental data obtained from the H₂-producing UASB reactor, whereas it was validated using independent sets of performance data obtained from another H₂-producing UASB reactor. Subsequently, predictions were performed using the validated model to determine the effects of substrate concentration and HRT on the reactor performance. The simulation results demonstrate that the model was able to effectively describe the daily variations of the UASB reactor performance, and to predict the steady-state reactor performance at various substrate concentrations and HRTs.     

Control of PEM fuel cell power system using sliding mode and super-twisting algorithms

Traditional sliding mode controller applied to a DC/DC boost converter for the improvement and optimization of the proton exchange membrane fuel cell (PEMFC) system efficiency, has the drawback of chattering phenomenon. Thus, based on the analysis of the mathematical model of PEMFC, this paper addresses the second order super twisting algorithm (STA) as a solution of chattering reduction, Stability of the closed loop system is analytically proved using Lyapunov approach for the proposed controller. The model and the controllers are implemented in the MATLAB and SIMULINK environment. A comparison of results indicates that the suggested approach has considerable advantages compared to the classical sliding mode control.     

Parameter design on the multi-objectives of PEM fuel cell stack using an adaptive neuro-fuzzy inference system and genetic algorithms

This work considers a design of a PEM fuel cell (PEMFC) stack that consists of 10 cells and expects to carry out an analysis of performance. In this work, PEMFC performance as affected by different combinations of control factors, such as the cathode and anode operating pressures, the humidification temperatures, and the stoichiometric flow ratio of reaction gas, is studied. On the PEMFC stack performance, the gas supply that is expected to be the minimum and the output power that is hoped to be the maximum are a result of the demand of the multi-objectives characteristics. Due to the Taguchi orthogonal array, the screen experiment is carried out by using a fractional factorial design in order to determine main factors and interaction effects first, and then the robust design is conducted. The intelligent parameter design is developed via an Adaptive Neuro-Fuzzy Inference System combined with the definition of percentage reduction of quality loss (PRQL) in order to supply a fitness function to the genetic algorithms (GA). The best parameter design is proposed after an analysis and comparison is conducted. Finally, the adaptability of prediction for the model created by this approach is confirmed by the confirmation experiment. This work shows that the PEMFC performance is improved by 35.8% via the average PRQL.     

Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm

This paper presents a model-based supervisory and optimal control strategy for central chiller plants to enhance their energy efficiency and control performance. The optimal strategy is formulated using simplified models of major components and the genetic algorithm (GA). The simplified models are used as the performance predictors to estimate the system energy performance and response to the changes of control settings and working conditions. Since the accuracy of the models has significant impacts on the overall prediction results, the models used are linear in the parameters and the recursive least squares (RLS) estimation technique with exponential forgetting is used to identify and update the model parameters online. That is to ensure that the linear models can provide reliable and accurate estimates when working condition changes. The GA, as a global optimization tool, is used to solve the optimization problem and search for globally optimal control settings. The performance of this strategy is tested and evaluated in a simulated virtual system representing the actual central chiller plant in a super high-rise building under various working conditions. The results showed that this strategy can save about 0.73–2.55% daily energy of the system studied, as compared to a reference strategy using conventional settings.     

Parameter optimization of the biohydrogen real time power generating system using differential evolution algorithm

By comparing the differential evolution and genetic algorithms, this study attempts to optimize estimation of a biohydrogen real time power generating system in which circuit parameters fluctuate with operating temperature and current density. Based on uses of the differential evolution algorithm method, optimal estimation of the circuit parameters is achieved by data from a V–I characteristic experiment on the proposed biohydrogen real time power generating system. The circuit feature is then solved by formulating the estimated circuit parameters based on Kirchhoff’s law to elucidate its feature of the biohydrogen real time power generating system and results show that DE is faster than GA and more accurate. Next, the estimated V–I characteristics are compared with measurement results to demonstrate the feasibility of the proposed method.     

Compact modeling approach using genetic algorithms for accurate thermal simulation

In this paper, we propose a technique that uses thermal measurement results for improved accuracy in thermal simulation of electronic apparatus. Because the modeling of the electronic components in such apparatus has hitherto been very poor, the thermal simulation results cannot achieve the required accuracy. To solve this problem, we first represent a component as a set of cubic blocks with equivalent thermal conductivity and contact thermal resistance values, and then identify these values by using the thermal measurement results for the component. We regard the identification of parameters as an optimization problem that involves minimizing the difference between the predicted and measured results. To solve the problem, we combine genetic algorithms and a thermal simulation tool. Our technique was successfully applied to the construction of an accurate thermal model, which we validated by using thermal measurement results. © 2000 Scripta Technica, Heat Trans Asian Res, 30(1): 28–39, 2001     

Optimized rule-based energy management for a polymer electrolyte membrane fuel cell/battery hybrid power system using a genetic algorithm

The polymer electrolyte membrane fuel cell (PEMFC) coupled with the battery is a promising hybrid power system for future energy supply application. Fuel cell durability, battery charge sustenance, and fuel consumption strongly rely on the energy management strategy (EMS). This paper puts forward an optimized rule-based EMS using genetic algorithm (GA) to optimally allocate the power between the fuel cell and the battery system. Control variables in real-time rule-based EMS are optimally adjusted with single objective of battery charge sustenance considering the fuel cell durability and efficiency. The proposed optimized rule-based EMS is simulated and experimentally verified via MATLAB/Simulink and LabVIEW-based experimental rig, respectively. The conventional rule-based EMS, fuzzy logic EMS, and dynamic programming (DP) EMS are also examined for comparison. The comparison results elucidate that the optimized rule-based EMS realizes a large performance improvement over the conventional rule-based and fuzzy logic EMSs. Near optimal performance is verified compared with DP EMS in terms of fuel economy, battery charge sustenance, fuel cell efficiency, and system durability. The combination of rule-based EMS and GA optimization algorithm has the advantage of having expert experience and global optimization properties, realizing optimal power allocation in real-time application with lower computation burden, which could be applied easily to other EMS system without loss of validity.     

Integration of genetic algorithm,computer simulation and design of experiments for forecasting electrical energy consumption

This study presents an integrated algorithm for forecasting monthly electrical energy consumption based on genetic algorithm (GA), computer simulation and design of experiments using stochastic procedures. First, time-series model is developed as a benchmark for GA and simulation. Computer simulation is developed to generate random variables for monthly electricity consumption. This is achieved to foresee the effects of probabilistic distribution on monthly electricity consumption. The GA and simulated-based GA models are then developed by the selected time-series model. Therefore, there are four treatments to be considered in analysis of variance (ANOVA) which are actual data, time series, GA and simulated-based GA. Furthermore, ANOVA is used to test the null hypothesis of the above four alternatives being equal. If the null hypothesis is accepted, then the lowest mean absolute percentage error (MAPE) value is used to select the best model, otherwise the Duncan Multiple Range Test (DMRT) method of paired comparison is used to select the optimum model, which could be time series, GA or simulated-based GA. In case of ties the lowest MAPE value is considered as the benchmark. The integrated algorithm has several unique features. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE, whereas previous studies consider the best-fit GA model based on MAPE or relative error results. Second, the proposed algorithm may identify conventional time series as the best model for future electricity consumption forecasting because of its dynamic structure, whereas previous studies assume that GA always provide the best solutions and estimation. To show the applicability and superiority of the proposed algorithm, the monthly electricity consumption in Iran from March 1994 to February 2005 (131 months) is used and applied to the proposed algorithm.     

Geometric optimization of thermoelectric coolers in a confined volume using genetic algorithms

The demand for thermoelectric coolers (TEC) has grown significantly because of the need for a steady, low-temperature operating environment for various electronic devices such as laser diodes, semiconductor equipment, infrared detectors and others. The cooling capacity and its coefficient of performance (COP) are both extremely important in considering applications. Optimizing the dimensions of the TEC legs provides the advantage of increasing the cooling capacity, while simultaneously considering its minimum COP. This study proposed a method of optimizing the dimensions of the TEC legs using genetic algorithms (GAs), to maximize the cooling capacity. A confined volume in which the TEC can be placed and the technological limitation in manufacturing a TEC leg were considered, and three parameters––leg length, leg area and the number of legs––were taken as the variables to be optimized. The constraints of minimum COP and maximum cost of the material were set, and a genetic search was performed to determine the optimal dimensions of the TEC legs. This work reveals that optimizing the dimensions of the TEC can increase its cooling capacity. The results also show that GAs can determine the optimal dimensions according to various input currents and various cold-side operating temperatures.